Classification of Multidrug-Resistance Reversal Agents Using Structure-Based Descriptors and Linear Discriminant Analysis

Abstract

Linear discriminant analysis is used to generate models to classify multidrug-resistance reversal agents based on activity. Models are generated and evaluated using multidrug-resistance reversal activity values for 609 compounds measured using adriamycin-resistant P388 murine leukemia cells. Structure-based descriptors numerically encode molecular features which are used in model formation. Two types of models are generated: one type to classify compounds as inactive, moderately active, and active (three-class problem) and one type to classify compounds as inactive or active without considering the moderately active class (two-class problem). Two activity distributions are considered, where the separation between inactive and active compounds is different. When the separation between inactive and active classes is small, a model based on nine topological descriptors is developed that produces a classification rate of 83.1% correct for an external prediction set. Larger separation between active and inactive classes raises the prediction set classification rate to 92.0% correct using a model with six topological descriptors. Models are further validated through Monte Carlo experiments in which models are generated after class labels have been scrambled. The classification rates achieved demonstrate that the models developed could serve as a screening mechanism to identify potentially useful MDRR agents from large libraries of compounds.

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

“…Those from the class of electrotopological state constitute the largest percentage of the descriptors selected, which is consistent with a linear discriminant analysis of structure-based descriptors for multidrug resistant (MDR) agents that showed that 60% of the molecular descriptors important for MDR are topological in nature. 71 A large variety of descriptors in this class, such as those of different functional groups and hydrophobic properties, are important for characterization of pharmacodynamic, pharmacokinetic, and toxicological properties. 71,72 There are also a substantial number of descriptors from the quantum chemical, connectivity, and geometric classes.…”

Three-Dimensional Quantitative Structure-Activity Relationships of Inhibitors of P-Glycoprotein

“…Similarly, 19 propafenone type P-gp inhibitors were then used to confirm the requirement for a carbonyl oxygen, suggested to form a hydrogen bond with P-gp (Chiba et al, 1996). Others have used MUL-TICASE to determine important substructural features like CH 2 -CH 2 -N-CH 2 -CH 2 (Klopman et al, 1997), and linear discriminant analysis with topological descriptors (Bakken and Jurs, 2000). In 1997, the first 3D-QSAR analysis of phenothiazines and related drugs known to be P-gp inhibitors was described previously (Pajeva and Wiese, 1997).…”

Construction of an Efficacious Model for a Nondestructive Identification of Traditional Chinese Medicines Liuwei Dihuang Pills from Different Manufacturers Using Near-infrared Spectroscopy and Moving Window Partial Least-squares Discriminant Analysis

“…Linear discriminant analysis (LDA) 19,20 is a well-known technique for dimensionality reduction and feature extraction. The basic principle of LDA is to seek a set of orthogonal latent variables to represent the original feature space.…”